How do you calculate eigenfaces?
To create a set of eigenfaces, one must:
- Prepare a training set of face images.
- Subtract the mean.
- Calculate the eigenvectors and eigenvalues of the covariance matrix S.
- Choose the principal components.
- k is the smallest number that satisfies.
What is OpenCV face recognition?
OpenCV is a video and image processing library and it is used for image and video analysis, like facial detection, license plate reading, photo editing, advanced robotic vision, and many more.
What do eigenfaces represent?
Specifically, the eigenfaces are the principal components of a distribution of faces, or equivalently, the eigenvectors of the covariance matrix of the set of face images, where an image with N pixels is considered a point (or vector) in N-dimensional space.
How does an OpenCV work?
OpenCV is a great tool for image processing and performing computer vision tasks. It is an open-source library that can be used to perform tasks like face detection, objection tracking, landmark detection, and much more. It supports multiple languages including python, java C++.
What is eigenfaces Python?
Eigenfaces are calculated by estimating the principal components of the dataset of facial images. They are used for applications like Face Recognition and Facial Landmark Detection.
What is cascade classifier OpenCV?
It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images.
What is PCA in eigenfaces?
PCA (Principal Component Analysis) is a dimensionality reduction technique that was proposed by Pearson in 1901. It uses Eigenvalues and EigenVectors to reduce dimensionality and project a training sample/data on small feature space.
How are eigenfaces used?
Eigenfaces is a method that is useful for face recognition and detection by determining the variance of faces in a collection of face images and use those variances to encode and decode a face in a machine learning way without the full information reducing computation and space complexity.
How to get eigenfaces from eigenvectors in OpenCV?
We can reshape these Eigenvectors into 100 x 100 x 3 images to obtain EigenFaces. The PCA class in OpenCV allows us to compute the principal components of a data matrix. Read the documentation for different usages. Here we are discussing the most common way to use the PCA class.
Can eigenfaces be used for face recognition?
In this article, we have explored EigenFaces in depth and how it can be used for Face recognition and developed a Python demo using OpenCV for it. Facial recognition techonology is used to recognise a person using an image or a video.
Where are the eigenfaces in C++ and Python?
Next, we reshape the mean vector to obtain the average face in line 46 of the C++ and Python code. We also, reshape, the Eigen Vectors to obtain the EigenFaces in lines 48-54 in both versions of the code.
How to calculate the weights of the eigenfaces?
The weights are calculated by the dot product of the mean subtracted image vector and the EigenVectors. Finally, the weighted EigenFaces are added to the average face. If you liked this article and would like to download code (C++ and Python) and example images used in this post, please click here.